What GPU do I need to run Alibaba-NLP/gte-Qwen2-1.5B-instruct?
1.8B parameters, published in F32. View on Hugging Face
Set up gte-Qwen2-1.5B-instructgte-Qwen2-1.5B-instruct is published by Alibaba-NLP on Hugging Face, with 874,483 downloads and 237 likes to date. It's a Qwen2ForCausalLM model built for sentence-similarity, published natively in F32.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run gte-Qwen2-1.5B-instruct at its published (F32) precision: 1× RTX 3070 on simplepod, at $0.050/hr per GPU ($0.050/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
gte-Qwen2-1.5B-instruct: common questions
Does gte-Qwen2-1.5B-instruct fit on a 8 GB GPU?
Yes. At FP32 it needs 7.9 GB of VRAM, so an 8 GB card holds it with 0.1 GB to spare. A 6 GB card is not enough for it at FP32.
Can gte-Qwen2-1.5B-instruct run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 6.6 GB, or 7.9 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 3.3 GB, or 4.0 GB with overhead. That moves it onto a 6 GB card instead of an 8 GB one. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM gte-Qwen2-1.5B-instruct can run in?
1.0 GB, at INT4 (quantized), which fits a 6 GB card, against 7.9 GB at FP32. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Alibaba-NLP models
- gte-Qwen2-7B-instruct (7.6B, F32)
- Tongyi-DeepResearch-30B-A3B (30.5B, BF16)
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
